As the last step of network discovery, we describe our query node selection module. As in prior studies [1]}, [2]}, it is natural to select a query node that has a high degree centrality since it enables the resulting explored subgraph to grow fast. In our work, selecting a high degree node to query would also be beneficial in providing more samples to train our Siamese neural network. Nevertheless, we pay attention to the residual degree of a node at each query step \(t\) , defined as \(r_u^{(t)} = \hat{d}_u^{(t)} - d_u^{(t)}\) , rather than the estimated degree \(\hat{d}_u^{(t)}\)  itself, where \(d_u^{(t)}\)  denotes the degree of an observable node at the \(t\) -th query step. Obviously, the residual degree \(r_u\)  represents a better capability to explore more nodes when the inference accuracy is sufficiently high since the observable nodes in the explored subgraph no longer contribute to its growth.
